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» Mining top-n local outliers in large databases
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ISSTA
2009
ACM
15 years 4 months ago
Identifying bug signatures using discriminative graph mining
Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Al...
Hong Cheng, David Lo, Yang Zhou, Xiaoyin Wang, Xif...
VLDB
2005
ACM
180views Database» more  VLDB 2005»
15 years 2 months ago
Cache-conscious Frequent Pattern Mining on a Modern Processor
In this paper, we examine the performance of frequent pattern mining algorithms on a modern processor. A detailed performance study reveals that even the best frequent pattern min...
Amol Ghoting, Gregory Buehrer, Srinivasan Parthasa...
ICDCS
2002
IEEE
15 years 2 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
SIGMOD
2010
ACM
324views Database» more  SIGMOD 2010»
15 years 2 months ago
Similarity search and locality sensitive hashing using ternary content addressable memories
Similarity search methods are widely used as kernels in various data mining and machine learning applications including those in computational biology, web search/clustering. Near...
Rajendra Shinde, Ashish Goel, Pankaj Gupta, Debojy...
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
15 years 9 months ago
Cleaning disguised missing data: a heuristic approach
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
Ming Hua, Jian Pei